Evaluating the effects of intercrop management on weeds and soil aggregate stability during the establishment of semihardy grapevines in southern Quebec
Bibliographic record
Abstract
Living mulches from resident vegetation or intercrops could be used to control weeds and partially alleviate soil erosion during vineyard establishment in Quebec. However, their impact on grapevine yield and fruit quality is poorly documented. Growing semihardy grapevines is a challenge in southern Quebec as winter protection is necessary. Winter protection is provided either by hilling or by geotextiles and these methods determine what type of living mulch can be grown. Annual plant species are best suited for the former method, while perennial species are compatible with the latter. The aim of this study was to evaluate the effect of two grass living mulches (annual and perennial) on weed control and diversity, soil aggregate stability, vine growth, and fruit quality in comparison with cultivation and an unmanaged (weedy) control during vineyard establishment. The cultivation treatment was the most efficient weed control method and decreased weed species richness and diversity in comparison with intercrops. Maintaining a living mulch in the interrow, however, helped preserve soil aggregate stability better than cultivation did. Vine yield and fruit quality were not affected by any interrow weed management method. Consequently, the use of living mulches is a promising alternative to cultivation in the interrow during vineyard establishment in Quebec, Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".